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Poster
Neural Expectation Maximization
Klaus Greff · Sjoerd van Steenkiste · Jürgen Schmidhuber

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #12 #None

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects. We demonstrate that the learned representations are useful for next-step prediction.

Author Information

Klaus Greff (IDSIA)
Sjoerd van Steenkiste (The Swiss AI Lab - IDSIA)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)

Since age 15, his main goal has been to build an Artificial Intelligence smarter than himself, then retire. The Deep Learning Artificial Neural Networks developed since 1991 by his research groups have revolutionised handwriting recognition, speech recognition, machine translation, image captioning, and are now available to billions of users through Google, Microsoft, IBM, Baidu, and many other companies (DeepMind also was heavily influenced by his lab). His team's Deep Learners were the first to win object detection and image segmentation contests, and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition. His formal theory of fun & creativity & curiosity explains art, science, music, and humor. He has published 333 papers, earned 7 best paper/best video awards, the 2013 Helmholtz Award of the International Neural Networks Society, and the 2016 IEEE Neural Networks Pioneer Award. He is also president of NNAISENSE, which aims at building the first practical general purpose AI.

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